Search

Small open reading frames (smORFs) encoding proteins less than 100 amino acids (aa) are known to be important regulators of key cellular processes. However, their computational identification remains a challenge. Based on a comprehensive analysis of known prokaryotic small ORFs, we have developed the ProsmORF-pred resource which uses a machine learning (ML)-based method for prediction of smORFs in the prokaryotic genome sequences. ProsmORF-pred consists of two ML models, one for initiation site recognition in nucleic acid sequences upstream of putative start codons and the other uses translated amino acid sequences to decipher functional protein like sequences. The nucleotide sequence-based initiation site recognition model has been trained using longer ORFs (>100 aa) in the same genome while the ML model for identification of protein like sequences has been trained using annotated smORFs from Escherichia coli. Comprehensive benchmarking of ProsmORF-pred reveals that its performance is comparable to other state-of-the-art approaches on the annotated smORF set derived from 32 prokaryotic genomes. Its performance is distinctly superior to other tools like PRODIGAL and RANSEPS for prediction of newly identified smORFs which have a length range of 10–30 aa, where prediction of smORFs has been a major challenge. Apart from identification of smORFs in genomic sequences, ProsmORF-pred can also aid in functional annotation of the predicted smORFs based on sequence similarity and genomic neighbourhood similarity searches in ProsmORFDB, a well-curated database of known smORFs. ProsmORF-pred along with its backend database ProsmORFDB is available as a user-friendly web server (http://www.nii.ac.in/prosmorfpred.html). [ABSTRACT FROM AUTHOR]

Even though several in silico tools are available for prediction of the phosphorylation sites for mammalian, yeast or plant proteins, currently no software is available for predicting phosphosites for Plasmodium proteins. However, the availability of significant amount of phospho-proteomics data during the last decade and advances in machine learning (ML) algorithms have opened up the opportunities for deciphering phosphorylation patterns of plasmodial system and developing ML-based phosphosite prediction tools for Plasmodium. We have developed Pf-Phospho, an ML-based method for prediction of phosphosites by training Random Forest classifiers using a large data set of 12 096 phosphosites of Plasmodium falciparum and Plasmodium bergei. Of the 12 096 known phosphosites, 75% of sites have been used for training/validation of the classifier, while remaining 25% have been used as completely unseen test data for blind testing. It is encouraging to note that Pf-Phospho can predict the kinase-independent phosphosites with 84% sensitivity, 75% specificity and 78% precision. In addition, it can also predict kinase-specific phosphosites for five plasmodial kinases— Pf PKG, Plasmodium falciparum , Pf PKA, Pf PK7 and Pb CDPK4 with high accuracy. Pf-Phospho (http://www.nii.ac.in/pfphospho.html) outperforms other widely used phosphosite prediction tools, which have been trained using mammalian phosphoproteome data. It also has been integrated with other widely used resources such as PlasmoDB, MPMP, Pfam and recently available ML-based predicted structures by AlphaFold2. Currently, Pf-phospho is the only bioinformatics resource available for ML-based prediction of phospho-signaling networks of Plasmodium and is a user-friendly platform for integrative analysis of phospho-signaling along with metabolic and protein–protein interaction networks. [ABSTRACT FROM AUTHOR]

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase. [ABSTRACT FROM AUTHOR]

Motivation Even though genome mining tools have successfully identified large numbers of non-ribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) biosynthetic gene clusters (BGCs) in bacterial genomes, currently no tool can predict the chemical structure of the secondary metabolites biosynthesized by these BGCs. Lack of algorithms for predicting complex macrocyclization patterns of linear PK/NRP biosynthetic intermediates has been the major bottleneck in deciphering the final bioactive chemical structures of PKs/NRPs by genome mining. Results Using a large dataset of known chemical structures of macrocyclized PKs/NRPs, we have developed a machine learning (ML) algorithm for distinguishing the correct macrocyclization pattern of PKs/NRPs from the library of all theoretically possible cyclization patterns. Benchmarking of this ML classifier on completely independent datasets has revealed ROC–AUC and PR–AUC values of 0.82 and 0.81, respectively. This cyclization prediction algorithm has been used to develop SBSPKSv3, a genome mining tool for completely automated prediction of macrocyclized structures of NRPs/PKs. SBSPKSv3 has been extensively benchmarked on a dataset of over 100 BGCs with known PKs/NRPs products. Availability and implementation The macrocyclization prediction pipeline and all the datasets used in this study are freely available at http://www.nii.ac.in/sbspks3.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

With the recent discoveries of novel post-translational modifications (PTMs) which play important roles in signaling and biosynthetic pathways, identification of such PTM catalyzing enzymes by genome mining has been an area of major interest. Unlike well-known PTMs like phosphorylation, glycosylation, SUMOylation, no bioinformatics resources are available for enzymes associated with novel and unusual PTMs. Therefore, we have developed the novPTMenzy database which catalogs information on the sequence, structure, active site and genomic neighborhood of experimentally characterized enzymes involved in five novel PTMs, namely AMPylation, Eliminylation, Sulfation, Hydroxylation and Deamidation. Based on a comprehensive analysis of the sequence and structural features of these known PTM catalyzing enzymes, we have created Hidden Markov Model profiles for the identification of similar PTM catalyzing enzymatic domains in genomic sequences. We have also created predictive rules for grouping them into functional subfamilies and deciphering their mechanistic details by structure-based analysis of their active site pockets. These analytical modules have been made available as user friendly search interfaces of novPTMenzy database. It also has a specialized analysis interface for some PTMs like AMPylation and Eliminylation. The novPTMenzy database is a unique resource that can aid in discovery of unusual PTM catalyzing enzymes in newly sequenced genomes. Database URL: http://www.nii.ac.in/novptmenzy.html [ABSTRACT FROM AUTHOR]

Bacterial phosphothreonine lyases catalyze a novel posttranslational modification involving formation of dehydrobutyrine/dehyroalanine by β elimination of the phosphate group of phosphothreonine or phosphoserine residues in their substrate proteins. Though there is experimental evidence for presence of dehydro amino acids in human proteins, no eukaryotic homologs of these lyases have been identified as of today. A comprehensive genome-wide search for identifying phosphothreonine lyase homologs in eukaryotes was carried out. Our fold-based search revealed structural and catalytic site similarity between bacterial phosphothreonine lyases and BLES03 (basophilic leukemia-expressed protein 03), a human protein with unknown function. Ligand induced conformational changes similar to bacterial phosphothreonine lyases, and movement of crucial arginines in the loop region to the catalytic pocket upon binding of phosphothreonine-containing peptides was seen during docking and molecular dynamics studies. Genome-wide search for BLES03 homologs using sensitive profile-based methods revealed their presence not only in eukaryotic classes such as chordata and fungi but also in bacterial and archaebacterial classes. The synteny of these archaebacterial BLES03-like proteins was remarkably similar to that of type IV lantibiotic synthetases which harbor LanL-like phosphothreonine lyase domains. Hence, context-based analysis reinforced our earlier sequence/structure-based prediction of phosphothreonine lyase catalytic function for BLES03. Our in silico analysis has revealed that BLES03-like proteins with previously unknown function are novel eukaryotic phosphothreonine lyases involved in biosynthesis of dehydro amino acids, whereas their bacterial and archaebacterial counterparts might be involved in biosynthesis of natural products similar to lantibiotics. [ABSTRACT FROM AUTHOR]

Motivation: In silico prediction of site-specific kinase–substrate relationships (ssKSRs) is crucial for deciphering phosphorylation networks by linking kinomes to phosphoproteomes. However, currently available predictors for ssKSRs give rise to a large number of false-positive results because they use only a short sequence stretch around phosphosite as determinants of kinase specificity and do not consider the biological context of kinase–substrate recognition.Results: Based on the analysis of domain-specific kinase–substrate relationships, we have constructed a domain-level phosphorylation network that implicitly incorporates various contextual factors. It reveals preferential phosphorylation of specific domains by certain kinases. These novel correlations have been implemented in PhosNetConstruct, an automated program for predicting target kinases for a substrate protein. PhosNetConstruct distinguishes cognate kinase–substrate pairs from a large number of non-cognate combinations. Benchmarking on independent datasets using various statistical measures demonstrates the superior performance of PhosNetConstruct over ssKSR-based predictors.Availability and implementation: PhosNetConstruct is freely available at http://www.nii.ac.in/phosnetconstruct.html.Contact: deb@nii.res.inSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Motivation:In silico methods are being widely used for identifying substrates for various kinases and deciphering cell signaling networks. However, most of the available phosphorylation site prediction methods use motifs or profiles derived from a known data set of kinase substrates and hence, their applicability is limited to only those kinase families for which experimental substrate data is available. This prompted us to develop a novel multi-scale structure-based approach which does not require training using experimental substrate data. [ABSTRACT FROM PUBLISHER]